Cementitious materials possessing self-sensing properties conferred through the incorporation of conductive fillers have attracted significant interest in the research community due to their potential application to structural health monitoring of civil infrastructure. A key advantage of cementitious sensors is that they can be organically embedded within cementitious structures to provide extremely robust monitoring capabilities. Of interest to this paper, the integration of cementitious sensors could be critical in empowering the 3D printing of cementitious composites by enabling real-time monitoring of additive manufacturing. Such integration could be achieved by 3D printing self-sensing cementitious nodes during the fabrication process. 3D printed self-sensing cementitious sensors could also be topologically engineered to achieve new opportunities in sensing of existing and new concrete and other cementitious structures. This paper presents one of the earliest studies, if not the first, on the 3D printing of self-sensing functionalized composites. The self-sensing cementitious composite sensors are fabricated using a cementitious matrix doped with graphite (G) and carbon microfibers. Various mixtures are studied, in particular G-only, G with milled carbon microfibers (MCMF), G with chopped carbon microfibers (CCMF), and G with MCMF and CCMF. A study of the percolation curves reveals that it is possible to attain electrical percolation using these conductive particles to create strain-sensitive sensors by leveraging the boosted piezoresistive effect. A study of strain sensing performance conducted on various specimens fabricated using different mixture types reveals the optimal concentration of dopants under different types of conductive particles. It is found that the optimal mixture that yields optimal performance also improves the Young's Modulus of the specimens by 42.2% compared to cement-only specimens. Overall, this optimal mixture composed of 10% weight-to-cement G (10 wt% G), 0.250 wt% MCMF, and 0.125 wt% CCMF yielded a static gauge factor of 622, a resolution of 167 mu epsilon , an accuracy of 19.24 mu epsilon , and a Young's Modulus of 798 MPa.

3D printed self-sensing cementitious composites using graphite and carbon microfibers

D'Alessandro A.;Ubertini F.
2024

Abstract

Cementitious materials possessing self-sensing properties conferred through the incorporation of conductive fillers have attracted significant interest in the research community due to their potential application to structural health monitoring of civil infrastructure. A key advantage of cementitious sensors is that they can be organically embedded within cementitious structures to provide extremely robust monitoring capabilities. Of interest to this paper, the integration of cementitious sensors could be critical in empowering the 3D printing of cementitious composites by enabling real-time monitoring of additive manufacturing. Such integration could be achieved by 3D printing self-sensing cementitious nodes during the fabrication process. 3D printed self-sensing cementitious sensors could also be topologically engineered to achieve new opportunities in sensing of existing and new concrete and other cementitious structures. This paper presents one of the earliest studies, if not the first, on the 3D printing of self-sensing functionalized composites. The self-sensing cementitious composite sensors are fabricated using a cementitious matrix doped with graphite (G) and carbon microfibers. Various mixtures are studied, in particular G-only, G with milled carbon microfibers (MCMF), G with chopped carbon microfibers (CCMF), and G with MCMF and CCMF. A study of the percolation curves reveals that it is possible to attain electrical percolation using these conductive particles to create strain-sensitive sensors by leveraging the boosted piezoresistive effect. A study of strain sensing performance conducted on various specimens fabricated using different mixture types reveals the optimal concentration of dopants under different types of conductive particles. It is found that the optimal mixture that yields optimal performance also improves the Young's Modulus of the specimens by 42.2% compared to cement-only specimens. Overall, this optimal mixture composed of 10% weight-to-cement G (10 wt% G), 0.250 wt% MCMF, and 0.125 wt% CCMF yielded a static gauge factor of 622, a resolution of 167 mu epsilon , an accuracy of 19.24 mu epsilon , and a Young's Modulus of 798 MPa.
2024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1578357
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